Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate, such as a semiconductor wafer, using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. As semiconductor device size becomes smaller and smaller, it becomes critical to develop enhanced inspection and review devices and procedures. One such procedure includes classification and analysis of defects on a specimen, such as a wafer. As used throughout the present disclosure, the term “wafer” generally refers to substrates formed of a semiconductor or non-semiconductor material. For example, a semiconductor or non-semiconductor material may include, but are not limited to, monocrystalline silicon, gallium arsenide, and indium phosphide.
Defect review is a process by which a user reviews defects acquired by an inspector or inspection tool. Defect review requires the classification of defects and the differentiation, or separation of defect types based on a set of defect attributes. However, current defect classification approaches have a number of limitations. Prior approaches involve the visual analysis of one or more scanning electron microscope (SEM) images to assign a class code based on the type of defect observed during the human review process. Such a manual review and classification process suffers from a number of disadvantages. First, it is noted that typically images are classified at less than 1000 defects per hour by user. Given that a typical sample size can be greater than 4000 defects, the manual classification process is slow. In addition, this process is user intensive and is susceptible to human error.
As such, it would be advantageous to provide a system and method that provides improved defect classification that cures the shortfalls identified above.